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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Optimality study of a gust alleviation system for light wing-loading STOL aircraft

An analytical study was made of an optimal gust alleviation system that employs a vertical gust sensor mounted forward of an aircraft's center of gravity. Frequency domain optimization techniques were employed to synthesize the optimal filters that process the corrective signals to the flaps and elevator actuators. Special attention was given to evaluating the effectiveness of lead time, that is, the time by which relative wind sensor information should lead the actual encounter of the gust. The resulting filter is expressed as an implicit function of the prescribed control cost. A numerical example for a light wing loading STOL aircraft is included in which the optimal trade-off between performance and control cost is systematically studied.

Komoda, M.↗
NASA NTRS1984

Process Research of Polycrystalline Silicon Material (PROPSM)

A passivation process (hydrogenation) that will improve the power generation of solar cells fabricated from presently produced, large grain, cast polycrystalline silicon (Semix), a potentially low cost material are developed. The first objective is to verify the operation of a DC plasma hydrogenation system and to investigate the effect of hydrogen on the electrical performance of a variety of polycrystalline silicon solar cells. The second objective is to parameterize and optimize a hydrogenation process for cast polycrystalline silicon, and will include a process sensitivity analysis. The sample preparation for the first phase is outlined. The hydrogenation system is described, and some early results that were obtained using the hydrogenation system without a plasma are summarized. Light beam induced current (LBIC) measurements of minicell samples, and their correlation to dark current voltage characteristics, are discussed.

Culik, J. S.↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Developing a one-step, acid-free microbial process to recover and separate REE (CRADA Final Report)

The project aimed to develop and test microbial strains for increased bioleaching, bioconcentration, and/or bioaccumulation of REEs, and for enhanced separation of REEs from other metals and of individual REEs from one another. The team secured and tested unconventional REE feedstocks to evaluate strains/processes for bioleaching, bioconcentration, and bioaccumulation, including but not limited to wastewater, geothermal brines, red mud, and electronic waste. purified and chemically analyzed REE granules from the microbial platform; determined metabolite and biosynthetic impacts of growth with REEs and the composition of extracellular material(s) produced by platform strains; optimized the growth process with alternative carbon feedstocks to reduce the cost of the fermentation process; and scaled the process from 1 mL to 10 L.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Desalting biocrude for improved downstream processing toward marine fuel application

Hydrothermal liquefaction of wet wastes (e.g., food waste and sewage sludge) offers a promising avenue for producing biocrude, but the resulting product is often contaminated with metals and water, impacting the biocrude upgrading steps to produce final fuel (e.g., jet and diesel). Here this study investigates the use of aqueous solutions of formic acid for desalting biocrudes and improving their quality. The results show that this approach significantly reduces both metal and water content, enabling mild hydrotreatment with reduced hydrogen consumption to further enhance the biocrude's quality. A preliminary technoeconomic analysis based on these experimental results shows that even without optimization, this washing process can yield biocrude with a minimum selling price of $1.87/GGE. Overall, our study demonstrates the potential for using dilute formic acid aqueous solutions to improve the quality of biocrudes, which can lead to more efficient and cost-effective conversion into biofuels.

09 BIOMASS FUELS↗
DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and Blacksand Technology, LLC

The National Energy Technology Laboratory (NETL) and Black Sand Technology, LLC (Participant) will collaborate in the optimization of low-cost manufacturing routes to produce ultralow-oxygen reactive metal powders of M=Ti, Zr, and Nb using Mg or Ca as reduction agents. Reactive metals have a strong chemical affinity to oxygen, making it very challenging to produce high-purity metal powders of these elements with extremely low oxygen content. This project integrates multiscale modeling and experimental validation to provide fundamental understanding of the thermodynamic limits on the deoxygenation process through HDH and optimize the key deoxygenation parameters to achieve ultralow oxygen content ≤0.1 wt.% in the reactive metal powders. The collaboration will significantly expedite the optimization of deoxygenation processes, reducing the timeline for refining deoxygenation techniques for Ti, Zr and Nb and other reactive refractory metals by about 0.5 and 2 years, respectively. This accelerated timeline translates to substantial cost savings estimated at $\$$3 million in research and development expenditures.

36 MATERIALS SCIENCE↗
NASA NTRS2001

ISS-Crystal Growth of Photorefractive Materials (BSO): Critical Design Issues for Optimized Data Extraction from Space Experiments

Realization of the full potential of photorefractive materials in device technology is seriously impeded by our inability to achieve controlled formation of critical defects during single crystal growth and by difficulties in meeting the required degree of compositional uniformity on a micro-scale over macroscopic dimensions. The exact nature and origin of the critical defects which control photorefractivity could not as yet be identified because of gravitational interference. There exists, however, strong evidence that the density of defect formation and their spatial distribution are adversely affected by gravitational interference which precludes the establishment of quantifiable and controllable heat and mass transfer conditions during crystal growth. The current, NASA sponsored research at MIT is directed at establishing a basis for the development of a comprehensive approach to the optimization of property control during melt growth of photorefractive materials, making use of the m-g environment, provided in the International Space Station. The objectives to be pursued in m-g research on photorefractive BSO (Bi12SiO20) are: (a) identification of the x-level(s) responsible for photorefractivity in undoped BSO; (b) development of approaches leading to the control of x-level formation at uniform spatial distribution; (c) development of doping and processing procedures for optimization of the critical, application specific parameters, spectral response, sensitivity, response time and matrix stability. The presentation will focus on: the rationale for the justification of the space experiment, ground-based development efforts, design considerations for the space experiments, strategic plan of the space experiments, and approaches to the quantitative analysis of the space experiments.

Hyers, Robert W.↗
DOE OSTI2023

Optimizing 3d spin polarization of CoOOH by in situ Mo doping for efficient oxygen evolution reaction

Transition-metal oxyhydroxides are attractive catalysts for oxygen evolution reactions (OERs). Further studies for developing transition-metal oxyhydroxide catalysts and understanding their catalytic mechanisms will benefit their quick transition to the next catalysts. Herein, Mo-doped CoOOH was designed as a high-performance model electrocatalyst with durability for 20 h at 10 mA cm -2 . Additionally, it had an overpotential of 260 mV (glassy carbon) or 215 mV (nickel foam), which was 78 mV lower than that of IrO 2 (338 mV). In situ, Raman spectroscopy revealed the transformation process of CoOOH. Calculations using the density functional theory showed that during OER, doped Mo increased the spin-up density of states and shrank the spin-down bandgap of the 3d orbits in the reconstructed CoOOH under the electrochemical activation process, which simultaneously optimized the adsorption and electron conduction of oxygen-related intermediates on Co sites and lowered the OER overpotentials. Our research provides new insights into the methodical planning of the creation of transition-metal oxyhydroxide OER catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis

This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

LigninGraphs: lignin structure determination with multiscale graph modeling

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Post‐Annealing Treatment on Hydrothermally Grown Antimony Sulfoselenide Thin Films for Efficient Solar Cells

Herein, antimony sulfoselenide (Sb 2 (S, Se) 3 ) thin‐film solar cells are fabricated by a hydrothermal method followed by a post‐deposition annealing process at different temperatures and the impact of the annealing temperature on the morphological, structural, optoelectronic, and defect properties of the hydrothermally grown Sb 2 (S, Se) 3 films is investigated. It is found that a proper annealing temperature leads to high‐quality Sb 2 (S, Se) 3 films with large crystal grains, high crystallinity, preferred crystal orientation, smooth and uniform morphology, and reduced defect density. These results show that suppressing deep‐level defects is crucial to enhance solar cell performance. After optimizing the annealing process, Sb 2 (S, Se) 3 solar cells with an improved power conversion efficiency 2.04 to 8.48% are obtained.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Creep behavior of 316 L stainless steel manufactured by laser powder bed fusion

Additive manufacturing as a new processing technique can produce unique microstructure that is difficult to achieve using conventional techniques. Here, we have investigated the creep behavior of 316 L stainless steel produced by a laser powder bed fusion process at temperatures of 550, 600 and 650 °C and stresses between 175 and 300 MPa. We found that additively-manufactured 316 L stainless steel had a higher stress dependence of the minimum creep rate than conventionally-made Type 316 SS, which could be attributed to the dislocation cell structure resulting from the printing process. The dislocation cell structure was unstable under creep, evolving into a uniform dislocation structure under the test conditions. While internal porosity in AM 316 L SS may serve as nucleation sites of creep voids and may be responsible for a relatively lower creep life, additively-manufactured 316 SS did not show inferior creep ductility when compared with conventionally-made 316 SS. The creep life of AM 316 L SS could be improved by stabilizing dislocation cell structure and/or reducing internal porosity through an optimized additive manufacturing process.

316 L stainless steel↗
DOE OSTI2022

CRADA Number NFE-19-07845 with American Nanotechnologies, Inc. (CRADA Final Report)

Cooperative Research and Development Agreement (CRADA) NFE-19-07845 between Oak Ridge National Laboratory (ORNL) and American Nanotechnologies, Inc. (ANI) focused on studying the use of dielectrophoresis as a mechanism to purify nanoparticles, particularly semiconducting carbon nanotubes (CNTs). The successful development of low-cost purification processes is a significant bottleneck in the adoption of semiconducting CNTs in commercial semiconducting devices. ANI is a startup company developing scalable systems for performing nanoparticle dielectrophoresis, which has previously been used only in microfluidic devices. The work done under this CRADA is exploring the use of ANI’s patent resonant dielectrophoresis (rDEP) technology and its ability to purify semiconducting CNTs at large scale. While continued work is on-going, r-DEP has proven to be a viable way to control nanoparticles in bulk dispersions. Optimization of the process is now underway to reach minimum viable product and begin material sales. Additionally, ANI and ORNL continue to collaborate on leveraging this technology to reach down the value chain and create new commercial devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Efficient conversion of syngas to linear α-olefins by phase-pure χ-Fe5C2

Abstract Oil has long been the dominant feedstock for producing fuels and chemicals, but coal, natural gas and biomass are increasingly explored alternatives 1–3 . Their conversion first generates syngas, a mixture of CO and H 2 , which is then processed further using Fischer–Tropsch (FT) chemistry. However, although commercial FT technology for fuel production is established, using it to access valuable chemicals remains challenging. A case in point is linear α-olefins (LAOs), which are important chemical intermediates obtained by ethylene oligomerization at present 4–8 . The commercial high-temperature FT process and the FT-to-olefin process under development at present both convert syngas directly to LAOs, but also generate much CO 2 waste that leads to a low carbon utilization efficiency 9–14 . The efficiency is further compromised by substantially fewer of the converted carbon atoms ending up as valuable C 5 –C 10 LAOs than are found in the C 2 –C 4 olefins that dominate the product mixtures 9–14 . Here we show that the use of the original phase-pure χ-iron carbide can minimize these syngas conversion problems: tailored and optimized for the process of FT to LAOs, this catalyst exhibits an activity at 290 °C that is 1–2 orders higher than dedicated FT-to-olefin catalysts can achieve above 320 °C (refs. 12–15 ), is stable for 200 h, and produces desired C 2 –C 10 LAOs and unwanted CO 2 with carbon-based selectivities of 51% and 9% under industrially relevant conditions. This higher catalytic performance, persisting over a wide temperature range (250–320 °C), demonstrates the potential of the system for developing a practically relevant technology.

Science & Technology - Other Topics↗
NASA NTRS1988

The optimal polarizations for achieving maximum contrast in radar images

There is considerable interest in determining the optimal polarizations that maximize contrast between two scattering classes in polarimetric radar images. A systematic approach is presented for obtaining the optimal polarimetric matched filter, i.e., that filter which produces maximum contrast between two scattering classes. The maximization procedure involves solving an eigenvalue problem where the eigenvector corresponding to the maximum contrast ratio is an optimal polarimetric matched filter. To exhibit the physical significance of this filter, it is transformed into its associated transmitting and receiving polarization states, written in terms of horizontal and vertical vector components. For the special case where the transmitting polarization is fixed, the receiving polarization which maximizes the contrast ratio is also obtained. Polarimetric filtering is then applies to synthetic aperture radar images obtained from the Jet Propulsion Laboratory. It is shown, both numerically and through the use of radar imagery, that maximum image contrast can be realized when data is processed with the optimal polarimeter matched filter.

Swartz, A. A.↗
DOE OSTI2022

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

Underutilized wet waste is a promising feedstock for production of carbon-neutral or carbon-negative liquid transportation fuels. Arrested methanogenesis of wet wastes by microbes, which produces mixtures of C2-C8 carboxylic acids (volatile fatty acids, VFAs), is practiced industrially at a pilot scale and the VFA products can be catalytically upgraded to molecules suitable for use as transportation fuels (alcohols and alkanes) via sequential ketonization and hydrogenation, both steps of which have been demonstrated with high (>90%) yield at the lab scale. We present in this work a simulation-based decision-making framework which evaluates upgrading strategies of VFAs to alcohol- and hydrocarbon-based neat and blended diesel, jet, and gasoline fuels. The processes utilize sequential upgrading steps (ketonization and hydrogenation) accompanied by one distillation step (either separating VFAs before ketonization or alcohols after ketone hydrogenation) to form two liquid streams: a light alcohol stream best suited as a light-duty fuel and a heavy alcohol or hydrocarbon stream best suited as a diesel or jet fuel. Suitability of the liquid products as transportation fuels and maximum blending levels with petrofuels comes from simulation of critical fuel properties (boiling point, flash point, lower heating value, viscosity, melting point, water solubility, and cetane/octane number). Catalytic upgrading steps and fuel property mixing models used by the simulation are experimentally validated. We demonstrate the flexibility of the decision-making algorithm by evaluating processing scenarios for experimentally-observed VFA profiles with varied carbon chain length distributions. Processing scenarios are optimized for either maximum total renewable carbon utilization or production of heavy-duty (jet or diesel) fuels. We evaluate the economic viability and CO2 emissions of each proposed upgrading scenario using techno-economic and life cycle analyses. The decision-making framework developed in this work can also be used to down-select promising strategies for upgrading of other bio-based feedstocks to sustainable fuels and chemicals.

aviation fuel↗